面向动态非地面网络-无线功率传输（NTN-WPT）系统的实用能量调度的机器学习分层预测方法
ML-Based Hierarchical Prediction for Practical Energy Scheduling in Dynamic NTN-WPT Systems
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中文总结 AI 辅助
针对动态NTN-WPT系统的调度挑战，本文提出含三层预测框架的ML方法，采用MORL与MAPPO模型优化能量调度，仿真显示其权衡性能更优且鲁棒性良好。
中文摘要 AI 辅助
随着长距离无线功率传输（WPT）和天基能源技术的进步，将WPT融入非地面网络（NTN）形成的NTN-WPT，正成为下一代无线网络的有前景方案。本文提出一种能量调度方法，联合优化低地球轨道卫星向地面移动用户设备（UD）功率传输的能效、任务完成率和任务等待时间。为应对卫星与UD移动性及随机传播效应导致的信道不确定性带来的调度挑战，本文将问题分解为三层预测框架内的三个子问题：1）状态预测层预测UD和卫星状态；2）交互映射层采用图神经网络（GNN）建模能量传输效率；3）决策层确定能量分配方案。各层采用定制的机器学习（ML）方法。为平衡相互冲突的目标，本文采用多目标强化学习（MORL）技术，将其标量化为加权和奖励，把多目标问题转化为可处理的单目标问题。本文还引入结合自注意力与多智能体近端策略优化（MAPPO）的多智能体深度学习模型，以优化目标平衡。仿真结果表明，所提方法相比基线方法实现更优的整体权衡，在保持有竞争力的任务完成率和能效的同时降低了任务等待时间，且在高度可变条件下仍具鲁棒性。
英文摘要
With advancements in long-distance wireless power transfer (WPT) and space-based energy technologies, integrating WPT into non-terrestrial networks (NTNs), referred to as NTN-WPT, is emerging as a promising approach for next-generation wireless networks. This paper proposes an energy-scheduling approach that jointly optimizes energy efficiency, task completion rate, and task waiting time for power transfer from low Earth orbit satellites to terrestrial mobile user devices (UDs). To address scheduling challenges caused by satellite and UD mobility and channel uncertainty from stochastic propagation effects, we decompose the problem into three subproblems within a three-layer predictive framework: 1) a state prediction layer forecasts UD and satellite states; 2) an interaction mapping layer uses a graph neural network (GNN) to model energy transfer efficiency; and 3) a decision-making layer determines the energy allocation plan. Distinct machine learning (ML) methods are tailored to each layer. To balance the competing objectives, we adopt a multi-objective reinforcement learning (MORL) technique that scalarizes them into a weighted-sum reward, transforming the multi-objective problem into a tractable single-objective problem. We further introduce a multi-agent deep learning model integrating self-attention with multi-agent proximal policy optimization (MAPPO) to improve objective balancing. Simulation results show that the proposed approach achieves a better overall trade-off than baseline methods, maintaining competitive task completion rates and energy efficiency while reducing task waiting times, and remains robust under highly variable conditions.
发表机构
- School of Telecommunications Engineering, Xidian University（西安电子科技大学电信工程学院）
机构由 AI 辅助整理，请以论文原文为准。
